Physics of failure is a technique under the practice of reliability design that leverages the knowledge and understanding of the processes and mechanisms that induce failure to predict reliability and improve product performance. Other definitions of Physics of Failure include:
A science-based approach to reliability that uses modeling and simulation to design-in reliability. It helps to understand system performance and reduce decision risk during design and after the equipment is fielded. This approach models the root causes of failure such as fatigue, fracture, wear, and corrosion. An approach to the design and development of reliable product to prevent failure, based on the knowledge of root cause failure mechanisms. The Physics of Failure (PoF) concept is based on the understanding of the relationships between requirements and the physical characteristics of the product and their variation in the manufacturing processes, and the reaction of product elements and materials to loads (stressors) and interaction under loads and their influence on the fitness for use with respect to the use conditions and time.
Overview The concept of Physics of Failure, also known as Reliability Physics, involves the use of degradation algorithms that describe how physical, chemical, mechanical, thermal, or electrical mechanisms evolve over time and eventually induce failure. While the concept of Physics of Failure is common in many structural fields, the specific branding evolved from an attempt to better predict the reliability of early generation electronic parts and systems.
The beginning Within the electronics industry, the major driver for the implementation of Physics of Failure was the poor performance of military weapon systems during World War II. During the subsequent decade, the United States Department of Defense funded an extensive amount of effort to especially improve the reliability of electronics, with the initial efforts focused on after-the-fact or statistical methodology. Unfortunately, the rapid evolution of electronics, with new designs, new materials, and new manufacturing processes, tended to quickly negate approaches and predictions derived from older technology. In addition, the statistical approach tended to lead to expensive and time-consuming testing. The need for different approaches led to the birth of Physics of Failure at the Rome Air Development Center (RADC). Under the auspices of the RADC, the first Physics of Failure in Electronics Symposium was held in September 1962. The goal of the program was to relate the fundamental physical and chemical behavior of materials to reliability parameters.
Early history – integrated circuits The initial focus of physics of failure techniques tended to be limited to degradation mechanisms in integrated circuits. This was primarily because the rapid evolution of the technology created a need to capture and predict performance several generations ahead of existing product. One of the first major successes under predictive physics of failure was a formula developed by James Black of Motorola to describe the behavior of electromigration. Electromigration occurs when collisions of electrons cause metal atoms in a conductor to dislodge and move downstream of current flow (proportional to current density). Black used this knowledge, in combination with experimental findings, to describe the failure rate due to electromigration as
MTTF = A ( J − n ) e E a k T {\displaystyle {\text{MTTF}}=A(J^{-n})e^{\frac {E_{\text{a}}}{kT}}}
where A is a constant based on the cross-sectional area of the interconnect, J is the current density, Ea is the activation energy (e.g. 0.7 eV for grain boundary diffusion in aluminum), k is the Boltzmann constant, T is the temperature and n is a scaling factor (usually set to 2 according to Black). Physics of failure is typically designed to predict wearout, or an increasing failure rate, but this initial success by Black focused on predicting behavior during operational life, or a constant failure rate. This is because electromigration in traces can be designed out by following design rules, while electromigration at vias are primarily interfacial effects, which tend to be defect or process-driven. Leveraging this success, additional physics-of-failure based algorithms have been derived for the three other major degradation mechanisms (time dependent dielectric breakdown [TDDB], hot carrier injection [HCI], and negative bias temperature instability [NBTI]) in modern integrated circuits (equations shown below). More recent work has attempted to aggregate these discrete algorithms into a system-level prediction. TDDB: τ = τo(T) exp[ G(T)/ εox] where τo(T) = 5.4×10−7 exp(−Ea / kT), G(T) = 120 + 5.8/kT, and εox is the permittivity. HCI: λHCI = A3 exp(−β/VD) exp(−Ea / kT) where λHCI is the failure rate of HCI, A3 is an empirical fitting parameter, β is an empirical fitting parameter, VD is the drain voltage, Ea is the activation energy of HCI, typically −0.2 to −0.1 eV, k is the Boltzmann constant, and T is absolute temperature. NBTI: λ = A εoxm VTμp exp(−Ea / kT) where A is determined empirically by normalizing the above equation, m = 2.9, VT is the thermal voltage, μp is the surface mobility constant, Ea is the activation energy of NBTI, k is the Boltzmann constant, and T is the absolute temperature.
… excerpt ends here. Continue reading the full article.
